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Article
Computer Science, Artificial Intelligence
Javeria Naz et al.
Summary: This paper introduces a hybrid approach based on texture and deep learning features for computer-aided diagnosis of stomach diseases. The method extracts texture features and deep convolutional neural network features, which are then serially fused to obtain a strong feature vector, thereby improving diagnostic accuracy.
NEURAL PROCESSING LETTERS
(2023)
Article
Engineering, Chemical
Badiea Abdulkarem Mohammed et al.
Article
Medicine, General & Internal
Omneya Attallah
Summary: Retinopathy of prematurity (ROP) is a serious ocular problem in premature infants. This paper proposes an automated CAD tool called GabROP, which uses Gabor wavelets and multiple deep learning models for ROP diagnosis. GabROP analyzes fundus images using Gabor wavelets and trains three CNN models independently. The features from these models are fused using discrete cosine transform (DCT) to obtain the final diagnosis result. Experimental results demonstrate the accuracy and efficiency of GabROP.
Article
Computer Science, Information Systems
Rohit Kundu et al.
Summary: Cervical cancer, affecting over 500,000 women annually, poses a challenge for population-wide screening due to its tedious detection procedure. This study combines deep learning and a genetic algorithm to classify cervical cells, utilizing pre-trained convolutional neural networks to extract deep features and optimizing them with the genetic algorithm for feature selection and support vector machines for final classification. The proposed method has been validated on publicly available datasets and achieved promising results, demonstrating its reliability.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Omneya Attallah
Summary: An automated tool called RADIC was developed to train deep learning models using radiomics-generated images, improving the accuracy of COVID-19 diagnosis with a final accuracy of 99.4% and 99% on two benchmark datasets.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2023)
Article
Horticulture
Omneya Attallah
Summary: Tomatoes are valuable vegetables and an economic pillar for many countries. Automatic identification of tomato leaf diseases using deep learning models has been widely studied, but existing methods suffer from high computational complexity and large dimensionality. This study proposes a pipeline that utilizes compact convolutional neural networks and transfer learning for condensed and high-level representation. The pipeline also applies a hybrid feature selection approach to reduce dimensions and achieves high accuracy in tomato leaf disease identification.
Article
Computer Science, Information Systems
I. Onur Sigirci et al.
Summary: The detection of mitotic cells in breast cancer diagnosis is crucial, with this study comparing conventional and deep learning methods for automatic detection. The deep learning approach performed better in mitosis detection, achieving high recall, precision, and F-measure values. Additionally, a client/server-based framework was developed as a decision support system for pathologists in hospitals, enhancing the detection process of mitotic cells in histopathological images.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Biology
Omneya Attallah et al.
Summary: Pediatric medulloblastomas (MBs), the most common malignant brain tumors in children, are heterogenous and challenging to classify accurately based on histopathological images. This study combines textural analysis and deep learning techniques to improve the subtype identification of pediatric MBs. The automated pipeline proposed in this study shows an increased accuracy in classification compared to previous methods, providing a powerful tool for individualized therapies and identification of high-risk complications in children.
Article
Engineering, Biomedical
Orhan Yaman et al.
Summary: Cervical cancer is a common type of cancer in women worldwide, and early detection is crucial for treatment. This study proposes a method based on exemplar pyramid deep feature extraction for the diagnosis of cervical cancer, achieving high accuracy through machine learning and computer vision techniques in classifying and diagnosing cervical cells in pap-smear images.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Computer Science, Interdisciplinary Applications
Rishav Pramanik et al.
Summary: This article proposes a fuzzy distance-based ensemble approach composed of deep learning models for cervical cancer detection in PaP smear images. By applying three transfer learning models and ensemble techniques, higher performance is achieved compared to individual models.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Biology
Shervan Fekri-Ershad et al.
Summary: This paper presents a two-stage method for pap smear image classification, improving the accuracy of cervical cancer diagnosis. The method uses texture information and a deep neural network for image classification, and improves performance through an optimization algorithm. The results show high detection accuracy, insensitivity to image rotation, and short runtime for the proposed method.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Biology
Omneya Attallah
Summary: This paper proposes a new method, ECG-BiCoNet, for diagnosing COVID-19 using electrocardiogram (ECG) data. The results show that ECG data can be used to accurately diagnose COVID-19, which helps clinicians with automatic diagnosis and overcomes limitations of manual diagnosis.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Information Systems
Chen Zhao et al.
Summary: This study proposes a cervical cell image generation model based on taming transformers to provide high-quality cervical cancer datasets. Experimental results show that the proposed model achieves high classification accuracy on public cervical cancer datasets, and the generated images are of similar quality to the source dataset.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Engineering, Biomedical
Hanan Ahmed Hosni Mahmoud et al.
Summary: This paper introduces a fast hybrid fuzzy classification algorithm with feature reduction for medical images. By incorporating the quantum-based grasshopper computing algorithm (QGH) with feature extraction using fuzzy clustering technique (C-means), the proposed technique aims to integrate QGH method into cervical cancer detection based on image processing. Performance evaluation is conducted to assess the influence of the extracted features on classification precision.
APPLIED BIONICS AND BIOMECHANICS
(2022)
Article
Chemistry, Analytical
Omneya Attallah
Summary: Accurate and rapid diagnosis of COVID-19 is crucial for controlling its spread, reducing lockdown restrictions, and alleviating the burden on healthcare systems. This study introduces a novel automated diagnostic tool based on ECG data, utilizing deep learning models to achieve high accuracy in diagnosing COVID-19 at binary and multiclass levels.
Article
Engineering, Biomedical
Chuanwang Zhang et al.
Summary: This paper proposes a novel approach for the classification of cervical cells by utilizing a multi-domain hybrid deep learning framework. Results demonstrate that the proposed method achieves similar performance as state-of-the-art models in terms of accuracy, sensitivity, and specificity. The study showcases the potential of the proposed framework for early diagnosis of cervical cancer and validates the feasibility of multi-domain and hybrid features in clinical practice.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Medicine, General & Internal
Omneya Attallah et al.
Summary: Lung and colon cancers are leading causes of mortality and morbidity. This study proposes a framework based on multiple lightweight deep learning models to detect these cancers at an early stage, achieving a high accuracy of 99.6% through feature reduction and fusion techniques.
Review
Medicine, General & Internal
Nur Ain Alias et al.
Summary: Cervical cancer is a common disease in women worldwide, and early diagnosis plays a crucial role in treatment and prognosis. Research has shown that automated cervical screening using machine learning techniques, such as Pap smears, can improve accuracy and clinical outcomes.
Article
Biotechnology & Applied Microbiology
Hiam Alquran et al.
Summary: This study proposes a computer-aided diagnosis method for cervical cancer using deep learning and feature fusion. The proposed method includes image processing, feature extraction and selection, as well as classification using machine learning algorithms. Experimental results show that the proposed method achieves high accuracy in multiple categories.
BIOENGINEERING-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Omneya Attallah et al.
Summary: The quick and accurate diagnosis of COVID-19 is crucial for preventing its spread and improving treatment outcomes. This study proposes a deep learning-based pipeline called CoviWavNet, which utilizes 3D multiview CT slices and spectral-temporal information to enhance the diagnostic accuracy of COVID-19.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Hardware & Architecture
Thavavel Vaiyapuri et al.
Summary: In this study, a novel cervical cancer classification model based on deep learning and modified firefly optimization algorithm is proposed for automated classification of Pap smear images. Through steps such as denoising, image segmentation, and feature extraction, the model can accurately determine the presence of cervical cancer cells in the Pap smear images, and experimental validation highlighted its superior performance compared to other recent approaches.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Wanli Liu et al.
Summary: This paper proposes a deep learning-based framework called CVM-Cervix to perform cervical cell classification tasks, utilizing Convolutional Neural Network and Visual Transformer modules for local and global feature extraction, showing effectiveness and potential in the field of cervical Pap smear image classification.
PATTERN RECOGNITION
(2022)
Article
Health Care Sciences & Services
Omneya Attallah
Summary: With the current health crisis caused by the COVID-19 pandemic, patients' preference for limited contact with doctors or clinicians has led to the development of computer-aided facial diagnosis systems. This study introduces FaceDisNet, a novel system that utilizes deep learning techniques and a new public dataset to diagnose single and multiple diseases accurately without physical contact with patients. The high accuracy achieved by FaceDisNet demonstrates its reliability and potential for assisting physicians in manual diagnosis.
Article
Health Care Sciences & Services
Omneya Attallah
Summary: The accurate and rapid detection of novel coronavirus is crucial to prevent its spread, and artificial intelligence techniques can aid in this process. This study proposes a computer-assisted diagnostic framework based on deep learning and texture-based radiomics approaches. By fusing deep features from multiple convolutional neural networks, the diagnostic accuracy is improved. The performance of this framework allows radiologists to achieve fast and accurate diagnosis of coronavirus.
Article
Oncology
Hyuna Sung et al.
Summary: The global cancer burden in 2020 saw an estimated 19.3 million new cancer cases and almost 10.0 million cancer deaths. Female breast cancer surpassed lung cancer as the most commonly diagnosed cancer, while lung cancer remained the leading cause of cancer death. These trends are expected to rise in 2040, with transitioning countries experiencing a larger increase compared to transitioned countries due to demographic changes and risk factors associated with globalization and a growing economy. Efforts to improve cancer prevention measures and provision of cancer care in transitioning countries will be crucial for global cancer control.
CA-A CANCER JOURNAL FOR CLINICIANS
(2021)
Article
Computer Science, Artificial Intelligence
Chaorong Li et al.
Summary: This study proposes two promising methods (LCMoG) for improving face recognition performance by learning the covariance matrix of Gabor wavelet. One method uses a shallow Convolutional Neural Network to project covariance matrices into Euclidean space, while the other method embeds the covariance matrix in linear space using matrix logarithm and learns face features through Whitening Principal Component Analysis.
PATTERN RECOGNITION
(2021)
Article
Medicine, General & Internal
Omneya Attallah
Summary: Medulloblastoma (MB) is a dangerous malignant pediatric brain tumor. Precise diagnosis is essential. The computer-aided diagnosis system MB-AI-His combines deep learning and texture analysis for automatic diagnosis of pediatric MB and its subtypes from histopathological images efficiently and reliably.
Article
Computer Science, Hardware & Architecture
Hua Chen et al.
Summary: The study developed an AI system named CytoBrain for automatic screening of abnormal cervical cells to aid in clinical diagnosis. The system consists of three main modules: cervical cell segmentation, cell classification based on CompactVGG, and human-aided diagnosis. CompactVGG showed superior performance in terms of time efficiency and classification accuracy, supporting large-scale cervical cancer screening.
JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
(2021)
Article
Medicine, General & Internal
Omneya Attallah
Summary: Retinopathy of Prematurity (ROP) affects preterm neonates and could lead to blindness. Deep Learning (DL) can assist ophthalmologists in diagnosing ROP, and the automated diagnostic tool DIAROP achieved an accuracy of 93.2%.
Article
Computer Science, Artificial Intelligence
Omneya Attallah et al.
Summary: Breast cancer is a common cancer affecting females globally, and late diagnosis can lead to severe consequences. The Histo-CADx system proposed in this study successfully improves the accuracy of breast cancer diagnosis through two fusion stages.
PEERJ COMPUTER SCIENCE
(2021)
Article
Biology
Md Mamunur Rahaman et al.
Summary: Cervical cancer can be prevented and treated through manual screening and deep learning techniques. The proposed hybrid deep feature fusion technology achieved good results in classifying cervical cells.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Omneya Attallah et al.
Summary: Skin cancer rates are increasing annually and early diagnosis is crucial. Artificial intelligence techniques have shown success in developing automated dermatologist tools for accurate classification, potentially improving diagnosis accuracy.
CONTRAST MEDIA & MOLECULAR IMAGING
(2021)
Article
Multidisciplinary Sciences
Ankur Manna et al.
Summary: This research proposes an ensemble method for cervical cancer single cell and whole slide image classification by combining three different convolutional neural network architectures. The proposed model is evaluated on two publicly available datasets, showing high classification accuracy and sensitivity.
SCIENTIFIC REPORTS
(2021)
Article
Mathematical & Computational Biology
Omneya Attallah
Summary: Childhood medulloblastoma (MB) is a common malignant tumor, and early and accurate classification is crucial. CoMB-Deep combines deep learning and texture analysis feature extraction techniques to effectively classify MB and improve diagnostic accuracy.
FRONTIERS IN NEUROINFORMATICS
(2021)
Review
Computer Science, Theory & Methods
Laith Alzubaidi et al.
Summary: Deep learning has become the gold standard in the machine learning community, widely used in various domains and capable of learning massive data. Through a comprehensive survey, a better understanding of the most important aspects of deep learning is provided.
JOURNAL OF BIG DATA
(2021)
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Jiayi Lu et al.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2020)
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Wen Chen et al.
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(2020)
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Elima Hussain et al.
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Pin Wang et al.
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Qi Xu et al.
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Dina A. Ragab et al.
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Zhiqiong Wang et al.
Review
Health Care Sciences & Services
Syed Muhammad Anwar et al.
JOURNAL OF MEDICAL SYSTEMS
(2018)
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Radiology, Nuclear Medicine & Medical Imaging
Natalia Antropova et al.
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Ling Zhang et al.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2017)
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Fernando Roberti de Siqueira et al.
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Ahmedin Jemal et al.
CA-A CANCER JOURNAL FOR CLINICIANS
(2011)
Review
Oncology
Ahmedin Jemal et al.
CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION
(2010)
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Aabid Shariff et al.
JOURNAL OF BIOMOLECULAR SCREENING
(2010)
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C He et al.
IEEE TRANSACTIONS ON MULTIMEDIA
(2002)
